4/29/2023 0 Comments Intraction subsume main effect![]() ![]() Similar calculations and states can be said for tertile 3. less negative) for domestic cars (domestic = 1) than it is for foreign cars. So the fact that that interaction coefficient is positive implies that the effect on price of being in tertile 2 of mpg is larger (i.e. That last number is, of course, the 1domestic#2.mpg_tertile interaction coefficient. Rather, there are two different effects of being in tertile 2, one corresponding to foreign cars (domestic = 0), which is -2840.8, and the other corresponding to domestic cars (domestic = 1) which is -2840.8 + 490.2187. Similarly, -2840.8 is not the effect of being in tertile 2 of mpg on price there is no such effect. The number 1643.282 is the effect of domestic on price in those cars that fall in tertile 1 of mpg. There are several different effects of domestic, depending on the value of mpg_tertile. In fact, in an interaction model, there is no such thing as the effect of domestic. For example, the effect of "domestic" is not to reduce price by 1643.282. When using an interaction model you have to remember that the "main" effects do not mean what they mean in the corresponding model without the interaction term. The main effects of domestic and mpg_tertile are all negative, but the interaction terms have positive coefficients. (Digression: binning a continuous variable to use as a regression predictor is a bad idea, and I encourage you to reconsider using the original continuous variable instead, but I won't go on here about the reasons way.) So domestic plays the role of director having professional experience, and mpg_tertile plays the role of your binned promoters' stake variable. ![]() ![]() This is analogous to your situation: domestic is a dichotomous variable, mpg_tertile is a binned version of a continuous variable. ![]()
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